{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n",
      "sys.version_info(major=3, minor=7, micro=5, releaselevel='final', serial=0)\n",
      "numpy 1.16.4\n",
      "pandas 0.25.3\n",
      "sklearn 0.22\n",
      "tensorflow 2.0.0\n",
      "tensorflow_core.keras 2.2.4-tf\n",
      "Training Images ->  20160\n",
      "Validation Images ->  6048\n",
      "Test Images ->  56\n"
     ]
    }
   ],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import sys\n",
    "import tensorflow as tf\n",
    "import time\n",
    "import random\n",
    "import pathlib\n",
    "\n",
    "from tensorflow import keras\n",
    "\n",
    "print(tf.__version__)\n",
    "print(sys.version_info)\n",
    "for module in np, pd, sklearn, tf, keras:\n",
    "    print(module.__name__, module.__version__)\n",
    "\n",
    "\n",
    "training_path = pathlib.Path('./training3')\n",
    "validation_path = pathlib.Path('./valid2')\n",
    "test_path = pathlib.Path('./test_gray')\n",
    "\n",
    "train_image_paths = list(training_path.glob('*/*'))  \n",
    "valid_image_paths = list(validation_path.glob('*/*'))  \n",
    "test_image_paths = list(test_path.glob('*/'))  \n",
    "\n",
    "train_image_paths = [str(path) for path in train_image_paths]\n",
    "valid_image_paths = [str(path) for path in valid_image_paths]\n",
    "test_image_paths = [str(path) for path in test_image_paths]\n",
    "\n",
    "random.shuffle(train_image_paths)\n",
    "random.shuffle(valid_image_paths)\n",
    "random.shuffle(test_image_paths)\n",
    "\n",
    "train_image_count = len(train_image_paths)\n",
    "valid_image_count = len(valid_image_paths)\n",
    "test_image_count = len(test_image_paths)\n",
    "\n",
    "print(\"Training Images -> \", train_image_count)\n",
    "print(\"Validation Images -> \", valid_image_count)\n",
    "print(\"Test Images -> \", test_image_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['training3\\\\Nocturne\\\\295.jpg',\n",
       " 'training3\\\\LeBlanc\\\\93.jpg',\n",
       " 'training3\\\\Yorick\\\\138.jpg',\n",
       " 'training3\\\\Taliyah\\\\15.jpg',\n",
       " 'training3\\\\Taliyah\\\\349.jpg']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['valid2\\\\Sion\\\\44.jpg',\n",
       " 'valid2\\\\Amumu\\\\57.jpg',\n",
       " 'valid2\\\\Taric\\\\39.jpg',\n",
       " 'valid2\\\\Lucian\\\\87.jpg',\n",
       " 'valid2\\\\RekSai\\\\64.jpg']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['test_gray\\\\Kindred.jpg',\n",
       " 'test_gray\\\\Olaf.jpg',\n",
       " 'test_gray\\\\Brand.jpg',\n",
       " 'test_gray\\\\KhaZix.jpg',\n",
       " 'test_gray\\\\Syndra.jpg']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image_paths[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Aatrox',\n",
       " 'Amumu',\n",
       " 'Annie',\n",
       " 'Ashe',\n",
       " 'Azir',\n",
       " 'Brand',\n",
       " 'Braum',\n",
       " 'Diana',\n",
       " 'DrMundo',\n",
       " 'Ezreal',\n",
       " 'Ivern',\n",
       " 'Janna',\n",
       " 'Jax',\n",
       " 'Karma',\n",
       " 'KhaZix',\n",
       " 'Kindred',\n",
       " 'KogMaw',\n",
       " 'LeBlanc',\n",
       " 'Leona',\n",
       " 'Lucian',\n",
       " 'Lux',\n",
       " 'Malphite',\n",
       " 'Malzahar',\n",
       " 'Maokai',\n",
       " 'MasterYi',\n",
       " 'Nami',\n",
       " 'Nasus',\n",
       " 'Nautilus',\n",
       " 'Neeko',\n",
       " 'Nocturne',\n",
       " 'Olaf',\n",
       " 'Ornn',\n",
       " 'Qiyana',\n",
       " 'RekSai',\n",
       " 'Renekton',\n",
       " 'Senna',\n",
       " 'Singed',\n",
       " 'Sion',\n",
       " 'Sivir',\n",
       " 'Skarner',\n",
       " 'Soraka',\n",
       " 'Syndra',\n",
       " 'Taliyah',\n",
       " 'Taric',\n",
       " 'Thresh',\n",
       " 'Twitch',\n",
       " 'Varus',\n",
       " 'Vayne',\n",
       " 'Veigar',\n",
       " 'Vladimir',\n",
       " 'Volibear',\n",
       " 'Warwick',\n",
       " 'Yasuo',\n",
       " 'Yorick',\n",
       " 'Zed',\n",
       " 'Zyra']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_names = sorted(item.name for item in training_path.glob('*/') if item.is_dir())\n",
    "label_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training3\\Nocturne\\295.jpg  --->   Nocturne\n",
      "training3\\LeBlanc\\93.jpg  --->   LeBlanc\n",
      "training3\\Yorick\\138.jpg  --->   Yorick\n",
      "training3\\Taliyah\\15.jpg  --->   Taliyah\n",
      "training3\\Taliyah\\349.jpg  --->   Taliyah\n"
     ]
    }
   ],
   "source": [
    "training_image_labels = [pathlib.Path(path).parent.name for path in train_image_paths]\n",
    "for image, label in zip(train_image_paths[:5], training_image_labels[:5]):\n",
    "    print(image, ' --->  ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_labels_info = []\n",
    "for image_path, label in zip(train_image_paths, training_image_labels):\n",
    "    train_labels_info.append((image_path, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('training3\\\\Nocturne\\\\295.jpg', 'Nocturne'),\n",
      " ('training3\\\\LeBlanc\\\\93.jpg', 'LeBlanc'),\n",
      " ('training3\\\\Yorick\\\\138.jpg', 'Yorick'),\n",
      " ('training3\\\\Taliyah\\\\15.jpg', 'Taliyah'),\n",
      " ('training3\\\\Taliyah\\\\349.jpg', 'Taliyah')]\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "pprint.pprint(train_labels_info[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "valid2\\Sion\\44.jpg  --->   Sion\n",
      "valid2\\Amumu\\57.jpg  --->   Amumu\n",
      "valid2\\Taric\\39.jpg  --->   Taric\n",
      "valid2\\Lucian\\87.jpg  --->   Lucian\n",
      "valid2\\RekSai\\64.jpg  --->   RekSai\n"
     ]
    }
   ],
   "source": [
    "valid_image_labels = [pathlib.Path(path).parent.name for path in valid_image_paths]\n",
    "for image, label in zip(valid_image_paths[:5], valid_image_labels[:5]):\n",
    "    print(image, ' --->  ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_labels_info = []\n",
    "for image_path, label in zip(valid_image_paths, valid_image_labels):\n",
    "    valid_labels_info.append((image_path, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('valid2\\\\Sion\\\\44.jpg', 'Sion'),\n",
      " ('valid2\\\\Amumu\\\\57.jpg', 'Amumu'),\n",
      " ('valid2\\\\Taric\\\\39.jpg', 'Taric'),\n",
      " ('valid2\\\\Lucian\\\\87.jpg', 'Lucian'),\n",
      " ('valid2\\\\RekSai\\\\64.jpg', 'RekSai')]\n"
     ]
    }
   ],
   "source": [
    "pprint.pprint(valid_labels_info[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test_gray\\Kindred.jpg  --->   Kindred\n",
      "test_gray\\Olaf.jpg  --->   Olaf\n",
      "test_gray\\Brand.jpg  --->   Brand\n",
      "test_gray\\KhaZix.jpg  --->   KhaZix\n",
      "test_gray\\Syndra.jpg  --->   Syndra\n"
     ]
    }
   ],
   "source": [
    "test_image_labels = [path.split('\\\\')[1].split('.')[0] for path in test_image_paths]\n",
    "for image, label in zip(test_image_paths[:5], test_image_labels[:5]):\n",
    "    print(image, ' --->  ', label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_labels_info = []\n",
    "for image_path, label in zip(test_image_paths, test_image_labels):\n",
    "    test_labels_info.append((image_path, label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     filepath     class\n",
      "0  training3\\Nocturne\\295.jpg  Nocturne\n",
      "1    training3\\LeBlanc\\93.jpg   LeBlanc\n",
      "2    training3\\Yorick\\138.jpg    Yorick\n",
      "3    training3\\Taliyah\\15.jpg   Taliyah\n",
      "4   training3\\Taliyah\\349.jpg   Taliyah\n",
      "               filepath   class\n",
      "0    valid2\\Sion\\44.jpg    Sion\n",
      "1   valid2\\Amumu\\57.jpg   Amumu\n",
      "2   valid2\\Taric\\39.jpg   Taric\n",
      "3  valid2\\Lucian\\87.jpg  Lucian\n",
      "4  valid2\\RekSai\\64.jpg  RekSai\n",
      "                filepath    class\n",
      "0  test_gray\\Kindred.jpg  Kindred\n",
      "1     test_gray\\Olaf.jpg     Olaf\n",
      "2    test_gray\\Brand.jpg    Brand\n",
      "3   test_gray\\KhaZix.jpg   KhaZix\n",
      "4   test_gray\\Syndra.jpg   Syndra\n"
     ]
    }
   ],
   "source": [
    "train_df = pd.DataFrame(train_labels_info)\n",
    "valid_df = pd.DataFrame(valid_labels_info)\n",
    "test_df = pd.DataFrame(test_labels_info)\n",
    "\n",
    "train_df.columns = valid_df.columns =test_df.columns = ['filepath', 'class']\n",
    "\n",
    "print(train_df.head())\n",
    "print(valid_df.head())\n",
    "print(test_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 20160 validated image filenames belonging to 56 classes.\n",
      "Found 6048 validated image filenames belonging to 56 classes.\n",
      "Found 56 validated image filenames belonging to 56 classes.\n"
     ]
    }
   ],
   "source": [
    "height = 64\n",
    "width = 64\n",
    "channels = 1\n",
    "batch_size = 128\n",
    "num_classes = 56\n",
    "\n",
    "train_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    #像素值 都除以255\n",
    "    rescale = 1./255,\n",
    "    # 图片随机旋转 (5度以内)\n",
    "    rotation_range = 5,\n",
    "    # 图片左右位移  20%限度以内\n",
    "    width_shift_range = 0.2,\n",
    "    # 图片上下位移  20%限度以内\n",
    "    height_shift_range = 0.2,\n",
    "    # 图像剪切强度\n",
    "    shear_range = 0.2,\n",
    "    # 图像缩放强度\n",
    "    zoom_range = 0.2,\n",
    "    # 是否水平翻转\n",
    "    horizontal_flip = False,\n",
    "    # 放大缩小吼， 像素填充方式\n",
    "    fill_mode = 'nearest',\n",
    ")\n",
    "\n",
    "train_generator = train_datagen.flow_from_dataframe(train_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 2333,\n",
    "                                                    shuffle = True,\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    class_mode = \"categorical\")\n",
    "\n",
    "valid_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    ")\n",
    "valid_generator = valid_datagen.flow_from_dataframe(valid_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 2333,\n",
    "                                                    shuffle = True,\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    class_mode = \"categorical\")\n",
    "\n",
    "test_datagen = keras.preprocessing.image.ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    ")\n",
    "test_generator = test_datagen.flow_from_dataframe(test_df, directory = './',\n",
    "                                                    x_col = 'filepath',\n",
    "                                                    y_col = 'class',\n",
    "                                                    classes = label_names,\n",
    "                                                    target_size = (height, width),\n",
    "                                                    batch_size = batch_size,\n",
    "                                                    seed = 2333,\n",
    "                                                    shuffle = True,\n",
    "                                                    color_mode=\"grayscale\",\n",
    "                                                    class_mode = \"categorical\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Generator Sample ->  20160\n",
      "Validation Generator Sample ->  6048\n",
      "Test Generator Sample ->  56\n"
     ]
    }
   ],
   "source": [
    "train_num = train_generator.samples\n",
    "valid_num = valid_generator.samples\n",
    "test_num = test_generator.samples\n",
    "\n",
    "print(\"Training Generator Sample -> \", train_num)\n",
    "print(\"Validation Generator Sample -> \", valid_num)\n",
    "print(\"Test Generator Sample -> \", test_num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(128, 64, 64, 1) (128, 56)\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [1. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 1. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 1.]\n",
      " [0. 0. 1. ... 0. 0. 0.]]\n",
      "(128, 64, 64, 1) (128, 56)\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    x, y = train_generator.next()\n",
    "    print(x.shape, y.shape)\n",
    "    print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_12 (Conv2D)           (None, 64, 64, 16)        160       \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 64, 64, 16)        2320      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_6 (MaxPooling2 (None, 32, 32, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 32, 32, 32)        4640      \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 32, 32, 32)        9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 16, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_16 (Conv2D)           (None, 16, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_17 (Conv2D)           (None, 16, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 8, 8, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 4096)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 128)               524416    \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 56)                7224      \n",
      "=================================================================\n",
      "Total params: 603,432\n",
      "Trainable params: 603,432\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Conv2D(filters=16, kernel_size = 3, padding='same',\n",
    "                       activation = 'selu', input_shape = [width, height, channels]),\n",
    "    keras.layers.Conv2D(filters=16, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Conv2D(filters=32, kernel_size = 3, padding='same',\n",
    "                       activation = 'selu', input_shape = [width, height, channels]),\n",
    "    keras.layers.Conv2D(filters=32, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Conv2D(filters=64, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.Conv2D(filters=64, kernel_size = 3, \n",
    "                        padding='same', activation = 'selu'),\n",
    "    keras.layers.MaxPool2D(pool_size=2),\n",
    "    \n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(128, activation = 'selu'),\n",
    "    \n",
    "    keras.layers.Dense(num_classes, activation = 'softmax')\n",
    "])\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "             optimizer = \"adam\", metrics = ['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "157/157 [==============================] - 22s 137ms/step - loss: 2.3538 - accuracy: 0.3998 - val_loss: 1.6962 - val_accuracy: 0.5455\n",
      "Epoch 2/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 1.0445 - accuracy: 0.7178 - val_loss: 0.8661 - val_accuracy: 0.7477\n",
      "Epoch 3/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.5638 - accuracy: 0.8440 - val_loss: 0.5444 - val_accuracy: 0.8268\n",
      "Epoch 4/20\n",
      "157/157 [==============================] - 20s 129ms/step - loss: 0.3803 - accuracy: 0.8946 - val_loss: 1.3034 - val_accuracy: 0.6948\n",
      "Epoch 5/20\n",
      "157/157 [==============================] - 20s 125ms/step - loss: 0.3086 - accuracy: 0.9158 - val_loss: 2.0010 - val_accuracy: 0.5946\n",
      "Epoch 6/20\n",
      "157/157 [==============================] - 20s 125ms/step - loss: 0.2412 - accuracy: 0.9311 - val_loss: 0.6930 - val_accuracy: 0.8281\n",
      "Epoch 7/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.2407 - accuracy: 0.9327 - val_loss: 2.3099 - val_accuracy: 0.5936\n",
      "Epoch 8/20\n",
      "157/157 [==============================] - 20s 128ms/step - loss: 0.1659 - accuracy: 0.9533 - val_loss: 1.2734 - val_accuracy: 0.7384\n",
      "Epoch 9/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.1815 - accuracy: 0.9478 - val_loss: 1.5124 - val_accuracy: 0.7379\n",
      "Epoch 10/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.1525 - accuracy: 0.9575 - val_loss: 0.5198 - val_accuracy: 0.8517\n",
      "Epoch 11/20\n",
      "157/157 [==============================] - 20s 125ms/step - loss: 0.1286 - accuracy: 0.9638 - val_loss: 1.5428 - val_accuracy: 0.7402\n",
      "Epoch 12/20\n",
      "157/157 [==============================] - 20s 129ms/step - loss: 0.1367 - accuracy: 0.9607 - val_loss: 1.5350 - val_accuracy: 0.7404\n",
      "Epoch 13/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.1401 - accuracy: 0.9606 - val_loss: 0.2242 - val_accuracy: 0.9387\n",
      "Epoch 14/20\n",
      "157/157 [==============================] - 20s 125ms/step - loss: 0.1162 - accuracy: 0.9677 - val_loss: 1.0359 - val_accuracy: 0.8201\n",
      "Epoch 15/20\n",
      "157/157 [==============================] - 20s 125ms/step - loss: 0.1197 - accuracy: 0.9658 - val_loss: 1.5964 - val_accuracy: 0.7430\n",
      "Epoch 16/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.1357 - accuracy: 0.9631 - val_loss: 0.9280 - val_accuracy: 0.8243\n",
      "Epoch 17/20\n",
      "157/157 [==============================] - 20s 124ms/step - loss: 0.1308 - accuracy: 0.9640 - val_loss: 1.6599 - val_accuracy: 0.7196\n",
      "Epoch 18/20\n",
      "157/157 [==============================] - 20s 125ms/step - loss: 0.1231 - accuracy: 0.9672 - val_loss: 1.6620 - val_accuracy: 0.7550\n",
      "Epoch 19/20\n",
      "157/157 [==============================] - 20s 126ms/step - loss: 0.1044 - accuracy: 0.9710 - val_loss: 0.4117 - val_accuracy: 0.9091\n",
      "Epoch 20/20\n",
      "157/157 [==============================] - 20s 130ms/step - loss: 0.0858 - accuracy: 0.9764 - val_loss: 1.3394 - val_accuracy: 0.8067\n"
     ]
    }
   ],
   "source": [
    "epochs = 20\n",
    "#因为数据是generator 产生的 所以不能用fit函数\n",
    "history = model.fit_generator(train_generator, steps_per_epoch=train_num // batch_size,\n",
    "                             epochs=epochs, validation_data=valid_generator,\n",
    "                             validation_steps=valid_num//batch_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_learning_curves(history, label, epochs, min_value, max_value):\n",
    "    data = {}\n",
    "    data[label] = history.history[label]\n",
    "    data['val_'+label] = history.history['val_' + label]\n",
    "    \n",
    "    pd.DataFrame(data).plot(figsize = (8,5))\n",
    "    plt.grid(True)\n",
    "    plt.axis([0, epochs, min_value, max_value])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history, 'accuracy', epochs, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curves(history, 'loss', epochs, 0, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_and_preprocess_single_img(path):\n",
    "    # read the img through file path\n",
    "    image = tf.io.read_file(path)  \n",
    "    image = tf.image.decode_jpeg(image, channels=1)\n",
    "    # 原始图片大小为(128, 128, 3)，重设为(64, 64)\n",
    "    image = tf.image.resize(image, [64, 64])  \n",
    "    image = tf.cast(image, tf.float32) / 255.0  # 归一化到[0,1]范围\n",
    "    image = np.expand_dims(image, axis = 0) # since you have batch_size, so you need to expand your image\n",
    "    return image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_single_pic(path, show=False):\n",
    "    \n",
    "    if show:\n",
    "        import matplotlib.image as mpimg\n",
    "        plt.imshow(mpimg.imread(path))\n",
    "    image = load_and_preprocess_single_img(path)\n",
    "    predict_result = model.predict(image)\n",
    "    print(\"This is\", label_names[np.argmax(predict_result, axis=1)[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Olaf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pic_path = \"./test.jpg\"\n",
    "evaluate_single_pic(test_pic_path, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Aatrox\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pic_path2 = \"./card.jpg\"\n",
    "evaluate_single_pic(test_pic_path2, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "1/1 [==============================] - 0s 362ms/step - loss: 5.7908 - accuracy: 0.6071\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[5.790806293487549, 0.60714287]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sss = model.evaluate(test_generator)\n",
    "sss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "predicted --> real\n",
      "Nautilus --> Brand\n",
      "Vayne --> Syndra\n",
      "Amumu --> Zyra\n",
      "Ashe --> Volibear\n",
      "Ashe --> Yorick\n",
      "DrMundo --> Senna\n",
      "Veigar --> Vayne\n",
      "Nautilus --> Lucian\n",
      "Leona --> Malzahar\n",
      "Nautilus --> Jax\n",
      "Lucian --> Sivir\n",
      "Jax --> Azir\n",
      "DrMundo --> Braum\n",
      "Veigar --> Twitch\n"
     ]
    }
   ],
   "source": [
    "print(\"predicted --> real\")\n",
    "for _, row in test_df.iterrows():\n",
    "#     print(row['filepath'], row['class'])\n",
    "    image = load_and_preprocess_single_img('.\\\\' + row['filepath'])\n",
    "    predict_result = model.predict(image)\n",
    "    predicted_class = label_names[np.argmax(predict_result, axis=1)[0]]\n",
    "    if (predicted_class != (row['class'])):\n",
    "        print(predicted_class + \" --> \" + row['class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Sion\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_pic_path3 = \"./aaa.jpg\"\n",
    "evaluate_single_pic(test_pic_path3, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is Senna\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "evaluate_single_pic(\"./2.png\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow-GPU",
   "language": "python",
   "name": "tf2_gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
